Journal article
MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI
IEEE Transactions on Medical Imaging, Vol.39(4), pp.1268-1277
04/2020
DOI: 10.1109/TMI.2019.2946501
PMCID: PMC7894613
PMID: 31603819
Abstract
We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MR images. The proposed algorithm is a generalization of the existing MUSSELS algorithm with similar performance but significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace, thus exploiting the annihilation relations between shots. Due to the high computational complexity of the self-learned filter bank, we propose replacing it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the annihilation relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state-of-the-art methods while offering several orders of magnitude reduction in run-time.
Details
- Title: Subtitle
- MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI
- Creators
- Hemant Kumar AggarwalMerry P ManiMathews Jacob
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Medical Imaging, Vol.39(4), pp.1268-1277
- DOI
- 10.1109/TMI.2019.2946501
- PMID
- 31603819
- PMCID
- PMC7894613
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Grant note
- DOI: 10.13039/100005440, name: Center for Scientific Review; DOI: 10.13039/100005440, name: Center for Scientific Review
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070955302771
Metrics
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